CN109103936A - Optimal unit starting order calculation method after a kind of electric system is had a power failure on a large scale - Google Patents
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Abstract
Optimal unit starting order calculation method after having a power failure on a large scale the invention discloses a kind of electric system, it is comprised the following steps: 1) establishing the undirected graph model of power grid, divide band electric network and power failure network；2) setting search depth is integer D, and the bus set of power failure is found in this depth bounds；3) in step 2) median generatrix set non-black starting-up unit and critical load be ranked up；4) path from bus where the non-black starting-up unit with highest priority in electric network to step 3) is found；5) path of step 4) is set as having restored electricity, sets " recovery " for the non-black starting-up unit of highest priority in step 3)；6) step 2) -5 is repeated) until all non-black starting-up units and critical load restore electricity；7) Q-learning frame, Optimization Steps 1 are built) -6) unit starting order scheme obtained.The present invention solves the problems, such as that existing optimal unit restores computational efficiency in sequential decision process.
Description
Technical field
The present invention relates to the black starting-up processes after technical field of power systems, especially electric system large-scale blackout (i.e.
Restore non-black starting-up unit and critical load), optimal unit after specifically a kind of electric system based on intensified learning is had a power failure on a large scale
Start order calculation method.
Background technique
The interconnection of electric system improves reliability of operation and economy, while improving some potential danger.Its
In a main problem be that the failure of local system may be caused extensive due to incorrect protection or automatic equipment operation
Have a power failure.It even results in entire power grid paralysis.A series of power-off events are that the safe operation of electric system has issued warning.Although
A possibility that this massive blackout, is very small, but fundamentally, power failure is inevitable.The reinforcement of system structure is protected
The improvement of protection unit and white dynamic device can only reduce the probability that such accident occurs to a certain extent, but cannot avoid completely
The generation of power failure.Power supply, to reduce the loss of power failure bring, just at one of each bulk power grid problem encountered.For in power grid
It can quickly and in an orderly manner restore after large-area power-cuts or all power failure, related scheme must be formulated in advance, i.e. " black starting-up " scheme,
To accelerate resume speed to the maximum extent, to reduce bring economic loss due to power failure to the maximum extent.
After large-scale blackout occurs for electric system, there is generating set (Hydropower Unit, the coal of self-starting function by starting
Oil machine group etc., referred to as " black starting-up unit "), to non self starting generating set (such as fired power generating unit, it is referred to as " non-black to open
Motivation group ") and critical load (such as government, the key enterprises and institutions such as hospital) be powered, can gradually restore power train
The supply district of system finally restores the power supply of entire electric system.This process is also referred to as " black starting-up ".
With going deep into for theoretical research and engineering practice, the method for having investigated various black starting-ups, such as: it is opened up using network
It flutters analytical technology and expert system to analyze the raw salty problem of black-start scheme, and is opened under black starting-up DSS
That has sent out black-start scheme gives birth to salty and assessment system automatically.The system can give birth to automatically for the particular state of a certain actual electric network
At one group of black-start scheme, analysis ratiocination is carried out using analytic hierarchy process (AHP) and by the knowledge base that expertise is established, to scheme group
In each scheme carry out assessment sequence, for dispatcher carry out black starting-up recovery one scientific, visual decision references is provided.
Existing black starting-up decision system is often partial to guarantee the success rate of black starting-up and sacrifice the calculating of black starting-up
Speed.
Summary of the invention
Existing black starting-up decision system is solved in the success rate for guaranteeing black starting-up the object of the present invention is to provide a kind of
The problem of but sacrificing the speed of black starting-up simultaneously.Therefore, apply intensified learning to improve black starting-up speed in black starting-up, effect
Rate greatly improves speed to reach while guaranteeing success rate.It is accumulated by a large amount of examples of early period, when really black
When starting operation, optimal unit starting order calculation method after the electric system for directly generating optimal case is had a power failure on a large scale.
In order to solve the problems existing in background technology, the present invention adopts the following technical scheme: a kind of electric system is big
Optimal unit starting order calculation method after power failure, it is comprised the following steps:
(1) the undirected graph model of power grid is established, band electric network and power failure network is divided；
(2) setting search depth is integer D, such as (D=2), and the bus set of power failure is found in this depth bounds；
(3) according to technical-economic index in step (2) median generatrix set non-black starting-up unit and critical load carry out
Sequence；
(4) it is found by shortest path first (such as, but not limited to Dijstra algorithm) from band electric network to step
3) path of bus where the non-black starting-up unit (or critical load) of highest priority in；
(5) path of step (4) is set as having restored electricity, and the bus on the path is set as " charging ", it will
The non-black starting-up unit (or critical load) of highest priority is set as " recovery " in step 3), and the path is divided into band
In electric network；
(6) step (2)-(5) are repeated until all non-black starting-up units and critical load restore electricity；
(7) Q-learning frame is built, using Iteration algorithm off-line training frame model, Optimization Steps (1)-(6)
Unit starting order scheme obtained, and Q matrix needed for obtaining on-line decision.
After adopting the above technical scheme, the invention has the following advantages:
The present invention proposes a kind of superior " black starting-up " method, intensified learning by intensified learning
(Reinforcement Learning) is a kind of important machine learning method, it is in Study of Intelligent Robot Control, analysis and pre-
Survey etc. has a wide range of applications.After the Q-1earning algorithm of present invention application intensified learning, compare the prior art, have with
Lower advantage:
1, guarantee the success rate of " black starting-up ".
2, guarantee " black starting-up " strategy calculating speed, i.e., by table look-up 9 can determine next step need it is to be started non-black
Start unit, without carrying out cumbersome topology search and calculating again.
3, learning process the step of (i.e. the present invention program (7)) of Q-1earning can carry out offline, and the mistake tabled look-up
Journey can carry out on-line decision, improve the efficiency of the decision of " black starting-up ".
Therefore, the present invention solves existing " black starting-up " decision system while the success rate of guarantee " black starting-up " but
The problem of sacrificing the speed of black starting-up.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is 5 bus standard system construction drawings of the invention；
Fig. 2 is the starting unit schematic diagram of step 3 in the present invention；
Fig. 3 is the starting unit schematic diagram of step 4 in the present invention；
Fig. 4 is the starting unit schematic diagram of step 5 in the present invention；
Fig. 5 is the starting unit schematic diagram of step 6 in the present invention；
Fig. 6 is the structure chart of intensified learning frame in the present invention；
Fig. 7 is the reward path profile in the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with IEEE-5 bus-bar system
(referring to attached drawing) and specific embodiment, the present invention will be described in further detail.It should be appreciated that described herein specific
Embodiment example only to explain the present invention, is not intended to limit the present invention.
Example please refers to Fig. 1-Fig. 7 with IEEE-5 bus-bar system, and present embodiment uses following technical scheme: a kind of
Electric system optimal unit starting order calculation method after having a power failure on a large scale, it is comprised the following steps:
(1) the undirected graph model of IEEE-5 bus-bar system is established.
The bus collection of the system is combined into { 73107,73106,73105,73103,73103 }, has one to have black starting-up energy
73103) and one the unit (be located at bus 73107) of power, three non-black starting-up units (are located at bus 73106,73105 and
Critical load (is located at bus 73104).The connection figure of the system is as shown in Figure 1.Design parameter is as shown in table 1- table 4.
(2) search depth D=2 is set, finds the bus collection being not powered within this range.
Bus collection is combined into { 73103,73104,73106,73105 } by checking feasibility, the only (starting of bus 73106
It is required that 20MW) and 73105 (starting requires 10MW) non-black starting-up unit can next step start, referring to table 5.It is passed through by technology
Help index analysis, has less starting capacity and the higher non-black starting-up unit of climbing rate will be started with higher priority.?
That is trial is started non-black starting-up unit on bus 73105 in next step.
(3) non-black starting-up unit of the starting on bus 73105.Connection bus 73105 is found by Dijstra algorithm
Path.
By bus 3,4 (73107-73106-73105), starting is connected to 73105 unit, electrification in path after calculating
Component is shown in Fig. 2 with overstriking lines.
(4) search depth D=2.In the neighborhood, two non-black starting-up units (i.e. 73106 and 73103) and one are found
Critical load (i.e. 73104).Critical load is considered as that climbing rate is equal to zero.It, should be non-black based on economic index analysis
After starting unit starting (unless being provided with user-defined priority for critical load), restart critical load.Meanwhile
Non- black starting-up unit on 73103 is bigger than 73106 climbing rates, is shown in Table 6.Therefore, will start on 73103 in the step
Non- black starting-up unit.In Fig. 3, band electrical component is shown in figure with overstriking lines.
(5) search depth=2.In the neighborhood, a non-black starting-up unit (i.e. 73106) and a critical load are found
(i.e. 73104).
By the non-black starting-up unit in this step starting 73106.It is shown in Table 7.The element to restore electricity is in Fig. 4 with overstriking
Lines are shown.
(6) finally start the critical load on 73104.
Path 73103-73104 is set up, and energization element is indicated in Fig. 5 with overstriking lines, so far entire black starting-up process knot
Beam.
(7) Q-learning frame is built, Iteration algorithm off-line training frame model, optimization " black starting-up " side are based on
Case.
Firstly, building intensified learning frame, change corresponding four main points of learning model: state, movement, transition probability, reward,
That is E=<S, A, P, R>, in state StLower execution movement a is indicated are as follows: a=π (st), StThe probability of lower execution movement a is π (st,
A), there are Σaπ(st, a)=1.With γ decaying progressive awardAs calculating progressive award calculating side
Formula, that is, the expectation that each step for asking decay factor γ to act on is rewarded immediately, wherein rtIndicate the instant reward that t step obtains,
For progressive award, decay factor γ ∈ (0,1) embodies following value discount ratio of the reward at current time immediately.Use GtDescription
All rewards accumulations decaying of one section of markov decision process since t moment to end and, referred to as Reward ProgramFig. 6 describes intensified learning model.
Pass through cost functionState action long-term value is assessed.
The step of selecting generator in black starting-up every time is considered as to the process of intelligent agent selection movement A, outlet can be done by above step
Diameter figure, and add reward value, such as Fig. 7.
Q-learning algorithm is taken to be modeled.By being iterated to movement-state value function.Update mode
Are as follows: ((s, a) (r+ γ Q (s ', a ')-Q (s, a)) completes after updating training, will obtain optimal case+α Q by s, a)=Q.
To have been turned on set state as row, the unit that can star in next step or meet for column, establishes award matrix R.
R matrix is passed through into the available convergent Q matrix of iteration.
It can be seen that each optimal selection is all identical as aforementioned algorism selection made, saves and unit is searched
Rope, reads, and the tedious steps of classification have saved the time.
According to table 9, when being again confronted with the selection of unit recovery order after having a power failure on a large scale every time, do not need to carry out complicated calculating
The step of (i.e. the present invention program (1)~(6)), can determine next non-black starting-up unit to be restored by tabling look-up, and accelerate
The calculating process of black starting-up strategy.
There are different schemes, innovative points from existing " black starting-up " scheme by the present invention are as follows: by being based on Q-
Learning algorithm carries out the optimization of speed to " black starting-up " simultaneously in guarantee success rate.Solves existing " black starting-up " decision
The problem of system but sacrifices the speed of black starting-up while the success rate of guarantee " black starting-up ".
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiments being understood that.
Claims (3)
1. a kind of electric system optimal unit starting order calculation method after having a power failure on a large scale, which is characterized in that it is comprised the following steps:
(1) the undirected graph model of power grid is established, band electric network and power failure network is divided；
(2) setting search depth is integer D, and the bus set of power failure is found in this depth bounds；
(3) according to technical-economic index in step (2) median generatrix set non-black starting-up unit and critical load be ranked up；
(4) by shortest path first find from highest priority in electric network to step (3) non-black starting-up unit or
The path of bus where critical load；
(5) path of step (4) is set as having restored electricity, and the bus on the path is set as " charging ", by step
(3) the non-black starting-up unit or critical load of highest priority are set as " recovery " in, and the path is divided into band power grid
In network；
(6) step (2)-(5) are repeated until all non-black starting-up units and critical load restore electricity；
(7) Q-learning frame is built, using Iteration algorithm off-line training frame model, Optimization Steps (1)-(6) are obtained
Unit starting order scheme, and Q matrix needed for obtaining on-line decision.
2. a kind of electric system according to claim 1 optimal unit starting order calculation method, feature after having a power failure on a large scale
It is, the Q-learning frame based on Iteration algorithm carries out offline optimization to machine startup order, and when on-line decision only needs
It wants the Q matrix formed after Query Value iteration to obtain optimal unit starting order, no longer needs to suboptimization calculating.
3. a kind of electric system according to claim 1 optimal unit starting order calculation method, feature after having a power failure on a large scale
It is, the Q-learning frame based on Iteration algorithm optimizes machine startup order, and when on-line decision only needs to look into
The matrix formed after inquiry value iteration obtains optimal unit starting order, no longer needs to suboptimization calculating.
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| CN109871587A (en) * | 2019-01-21 | 2019-06-11 | 南京铭越创信电气有限公司 | A kind of method of the controlled off-the-line of electric system under extreme weather conditions |
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| CN109871587A (en) * | 2019-01-21 | 2019-06-11 | 南京铭越创信电气有限公司 | A kind of method of the controlled off-the-line of electric system under extreme weather conditions |
| CN109818352A (en) * | 2019-03-26 | 2019-05-28 | 南京铭越创信电气有限公司 | A kind of power distribution network power supply vehicle of meet an emergency dispatching method based on approximate dynamic programming algorithm |
| CN109818352B (en) * | 2019-03-26 | 2023-06-30 | 南京铭越创信电气有限公司 | Distribution network emergency power supply vehicle scheduling method based on approximate dynamic programming algorithm |
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